Towards Learning and Explaining Indirect Causal Effects in Neural Networks
Authors: Abbavaram Gowtham Reddy, Saketh Bachu, Harsharaj Pathak, Benin Godfrey L, Varshaneya V, Vineeth N Balasubramanian, Satyanarayan Kar
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on synthetic and real-world datasets demonstrate that the causal effects learned by our ante-hoc method better approximate the ground truth effects compared to existing methods. |
| Researcher Affiliation | Collaboration | Abbavaram Gowtham Reddy1, Saketh Bachu1, Harsharaj Pathak1, Benin L. Godfrey1, Varshaneya V2, Vineeth N. Balasubramanian1, Satyanarayan Kar2 1 Indian Institute of Technology Hyderabad, India 2 Honeywell, Bengaluru, India |
| Pseudocode | Yes | Algorithm 1: Pseudocode for training N Ind model |
| Open Source Code | Yes | Code is available at https://github.com/gautam0707/Learning-and Explaining-Indirect-Causal-Effects. |
| Open Datasets | Yes | We conduct experiments on a synthetic dataset, three well-known real-world benchmark datasets, and three industry-based simulated datasets. ... Auto-MPG: In this experiment, we work on Auto-MPG dataset (Dua and Graff 2017) ... Lung Cancer: In Lung Cancer dataset (Scutari and Denis 2014), whose causal graph is known (see Appendix)... Sachs: Sachs dataset consists of 11 protein types and their causal relationships. |
| Dataset Splits | No | The paper mentions using a 'training set' and refers to 'test data point' but does not specify explicit percentages or counts for training, validation, and test splits needed to reproduce the experiment. |
| Hardware Specification | No | The paper mentions 'industry-grade flight simulator' for some datasets but does not provide specific hardware details such as GPU/CPU models, memory, or detailed computer specifications used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) required to replicate the experiment. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings in the main text. |